Purpose

We perform clustering of features and perturbations to identify isoforms that exhibit similar perturbation outcomes.

Initialization

Libraries

library(magrittr)
library(tidyverse)
library(pheatmap)
library(bluster)

Parameters

set.seed(20210818)

## filtering options
## number of cells in perturbation
MIN_CELLS=30
## max NTPs with NA WUI
MAX_NTP_NA=0.20
## min avg TPM in NTPs
MIN_MEAN_TPM=20

## input files
FILE_SE_WUI="tbl/df_wui_kd6_essential_ui10.Rds"

## output files
FILE_OUT_TARGETS_RAW="tbl/df_target_raw_nnclusters_kd6_essential_ui10.csv"
FILE_OUT_TARGETS="tbl/df_target_nnclusters_kd6_essential_ui10.csv"
FILE_OUT_GENES="tbl/df_gene_nnclusters_kd6_essential_ui10.csv"
FILE_OUT_FINAL="img/heatmap-kd6-essential-nnclusters-labeled.pdf"

RDS_DWUI=sprintf("data/dwui/%s-dwui-target-gene-kd6-essential.Rds", format(Sys.time(), '%Y%m%d'))
RDS_ZDWUI=sprintf("data/dwui/%s-zdwui-target-gene-kd6-essential.Rds", format(Sys.time(), '%Y%m%d'))

## aesthetics
NCOLORS=100
COLORS_BWR <- colorRampPalette(c("blue", "white", "red"))(NCOLORS)
COLORS_MKY <- colorRampPalette(c("magenta", "black", "yellow"))(NCOLORS)
COLORS_YKM <- colorRampPalette(c("yellow", "black", "magenta"))(NCOLORS)
COLORS_RKG <- colorRampPalette(c("red", "black", "green"))(NCOLORS)
breaks_dwui <- seq(-0.5, 0.5, length.out=NCOLORS + 1)
breaks_zdwui <- seq(-4, 4, length.out=NCOLORS + 1)
breaks_zdwui_broad <- seq(-6, 6, length.out=NCOLORS + 1)
breaks_pca <- seq(-20, 20, length.out=NCOLORS + 1)

Data

Loading

df_wui <- readRDS(FILE_SE_WUI)

Preprocessing

sgid2gene <- df_wui %>%
    dplyr::select(sgID_AB, target_gene) %>%
    distinct(sgID_AB, target_gene) %>%
    deframe()

ens2gene <- df_wui %>%
    dplyr::select(gene_id, gene_name) %>%
    distinct(gene_id, gene_name) %>%
    deframe()

convert_rownames <- function (mat, in2out) {
    mat %>% set_rownames(in2out[rownames(.)])
}

convert_colnames <- function (mat, in2out) {
    mat %>% set_colnames(in2out[colnames(.)])
}

df_ntp <- filter(df_wui, target_gene == 'non-targeting') %>%
    group_by(gene_id, gene_name) %>%
    filter(mean(is.na(wui)) <= MAX_NTP_NA) %>%
    filter(mean(tpm) >= MIN_MEAN_TPM) %>%
    filter(!is.na(wui)) %>%
    summarize(mean_wui=weighted.mean(wui, n_cells), .groups='drop',
              sd_wui=sqrt(sum((wui-mean_wui)^2)/n()))

df_dwui <- df_wui %>%
    filter(target_gene != 'non-targeting',
           n_cells >= MIN_CELLS) %>%
    inner_join(df_ntp, by=c("gene_id", "gene_name")) %>%
    mutate(dwui=wui-mean_wui) %>%
    dplyr::select(gene_id, sgID_AB, wui, dwui)

wui_gene_target <- df_dwui %>%
    dplyr::select(gene_id, sgID_AB, wui) %>%
    pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="wui") %>%
    column_to_rownames("gene_id") %>%
    as.matrix

dwui_gene_target <- df_dwui %>%
    dplyr::select(gene_id, sgID_AB, dwui) %>%
    pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="dwui") %>%
    column_to_rownames("gene_id") %>%
    as.matrix 

zdwui_gene_target <- t(dwui_gene_target) %>% scale(center=FALSE) %>% t()

zdwui_gene_target_clean <- zdwui_gene_target
zdwui_gene_target_clean[is.na(zdwui_gene_target_clean)] <- 0

PCA

Compute PCA

res_pca <- BiocSingular::runPCA(t(zdwui_gene_target_clean), rank=30, center=FALSE)
mat_pca <- res_pca$x

Heatmap - All Components

pheatmap(mat_pca, 
         color=COLORS_YKM,
         breaks=breaks_pca, 
         fontsize_col=1, fontsize_row=1,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=TRUE, cluster_cols=TRUE)

### Compute Clusters

clusters_targets_raw <- clusterRows(mat_pca, NNGraphParam(k=5, cluster.fun="walktrap"))
names(clusters_targets_raw) <- rownames(mat_pca)

idx_clusters_targets <- order(clusters_targets_raw)

df_row_annots <- data.frame(cluster_id_target=clusters_targets_raw, 
                            row.names=names(clusters_targets_raw))

table(clusters_targets_raw)
## clusters_targets_raw
##   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
## 114  70  15   7  30  23  27 462  15  13  44  96  28  51 779  27  17  25  22   8 
##  21  22  23  24  25  26  27  28 
##  28  48  52  10  23  32   3   8

Export Target Clusters

df_wui %>%
    distinct(target_gene, target_gene_id, sgID_AB) %>%
    inner_join(tibble(sgID_AB=rownames(mat_pca), cluster_id=clusters_targets_raw), by="sgID_AB") %>%
    write_csv(FILE_OUT_TARGETS_RAW)

Plot Clusters

pheatmap(mat_pca[idx_clusters_targets,], 
         color=COLORS_YKM,
         breaks=breaks_pca, 
         fontsize_col=1, fontsize_row=1,
         annotation_row=df_row_annots,
         show_colnames=FALSE, show_rownames=FALSE,
         cluster_rows=FALSE, cluster_cols=TRUE, scale='none',
         cutree_rows=NA)

It appears clusters 8 and 15 represent sets of perturbations with null responses.

Z-DWUI Plot

Potential Null Response Clusters

We want to check the potential null-response clusters in the DWUI representation.

Cluster 8

idx_cluster <- clusters_targets_raw %>% `[`(. == 8) %>% names()
pheatmap(t(zdwui_gene_target[,idx_cluster]), 
         color=COLORS_YKM,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=1,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=TRUE, cluster_cols=TRUE)

Cluster 15

idx_cluster <- clusters_targets_raw %>% `[`(. == 15) %>% names()
pheatmap(t(zdwui_gene_target[,idx_cluster]), 
         color=COLORS_YKM,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=1,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=TRUE, cluster_cols=TRUE)

This looks mostly like noise in both clusters.

All Other Clusters

idx_cluster <- clusters_targets_raw %>% `[`(!(. %in%  c(8,15))) %>% names
pheatmap(t(zdwui_gene_target[,idx_cluster]), 
         color=COLORS_YKM,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=1,
         annotation_row=df_row_annots,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=TRUE, cluster_cols=TRUE)

This looks good. Now we need to eliminate the noise columns (readout genes).

Establish Regulating Perturbations

idx_signal_targets <- clusters_targets_raw %>% `[`(!(. %in%  c(8,15))) %>% names

We identify 836 genes as coherent response genes.

Cluster Response Genes

Reduced PCA

res_pca2 <- BiocSingular::runPCA(zdwui_gene_target_clean[,idx_signal_targets], rank=30, center=FALSE)
mat_pca2 <- res_pca2$x
pheatmap(t(mat_pca2), 
         color=COLORS_YKM,
         breaks=breaks_pca, 
         fontsize_col=1, fontsize_row=1,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=TRUE, cluster_cols=TRUE)

Compute Gene Clusters

clusters_genes <- clusterRows(mat_pca2, NNGraphParam(k=4, cluster.fun="walktrap"))
names(clusters_genes) <- rownames(mat_pca2)

idx_clusters_genes <- order(clusters_genes)

df_col_annots <- data.frame(cluster_id_gene=clusters_genes, 
                            row.names=names(clusters_genes))

table(clusters_genes)
## clusters_genes
##   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
## 282 101 120  53  19  23  28  36 272  26  44 181 439  82  19   7  16  14   9   4

Export Gene Clusters

df_wui %>%
    distinct(gene_name, gene_id) %>%
    inner_join(enframe(clusters_genes, name="gene_id", value="cluster_id"), by="gene_id") %>%
    write_csv(FILE_OUT_GENES)

Plot Clusters

pheatmap(t(mat_pca2[idx_clusters_genes,]), 
         color=COLORS_YKM,
         breaks=breaks_pca, 
         fontsize_col=1, fontsize_row=1,
         annotation_col=df_col_annots,
         show_colnames=FALSE, show_rownames=FALSE,
         cluster_rows=TRUE, cluster_cols=FALSE, scale='none')

Cluster 9

idx_cluster <- clusters_genes %>% `[`(. == 9) %>% names()
pheatmap(t(zdwui_gene_target[idx_cluster, idx_signal_targets]), 
         color=COLORS_YKM,
         breaks=breaks_zdwui,
         annotation_row=df_row_annots,
         fontsize_col=1, fontsize_row=1,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=TRUE, cluster_cols=TRUE)

Cluster 12

idx_cluster <- clusters_genes %>% `[`(. == 12) %>% names()
pheatmap(t(zdwui_gene_target[idx_cluster, idx_signal_targets]), 
         color=COLORS_YKM,
         breaks=breaks_zdwui,
         annotation_row=df_row_annots,
         fontsize_col=1, fontsize_row=1,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=TRUE, cluster_cols=TRUE)

Cluster 13

idx_cluster <- clusters_genes %>% `[`(. == 13) %>% names()
pheatmap(t(zdwui_gene_target[idx_cluster, idx_signal_targets]), 
         color=COLORS_YKM,
         breaks=breaks_zdwui,
         annotation_row=df_row_annots,
         fontsize_col=1, fontsize_row=1,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=TRUE, cluster_cols=TRUE)

Clusters 9, 12, and 13 appear to be mostly non-responsive.

Establish Signal Genes

idx_signal_genes <- clusters_genes %>% `[`(!(. %in%  c(9,12,13))) %>% names

We identify 883 genes as coherent response genes.

pheatmap(t(zdwui_gene_target[idx_signal_genes, idx_signal_targets]), 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=1,
         annotation_row=df_row_annots, 
         annotation_col=df_col_annots,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=TRUE, cluster_cols=TRUE, clustering_method="complete")

Recompute Target Clusters

zdwui_target_gene_final <- t(zdwui_gene_target_clean[idx_signal_genes, idx_signal_targets])
res_pca3 <- BiocSingular::runPCA(zdwui_target_gene_final, rank=30, center=FALSE)
mat_pca3 <- res_pca3$x

clusters_targets_final <- clusterRows(mat_pca3, BLUSPARAM=NNGraphParam(k=3, cluster.fun="walktrap"))
names(clusters_targets_final) <- rownames(mat_pca3)

clusters_genes_final <- clusters_genes %>% `[`(!(. %in% c(9,12,13)))

idx_clusters_targets_final <- clusters_targets_final %>% sort %>% names
idx_clusters_genes_final <- clusters_genes_final %>% sort %>% names

df_row_annots_final <- data.frame(cluster_id_target=clusters_targets_final, 
                                  row.names=names(clusters_targets_final))

table(clusters_targets_final)
## clusters_targets_final
##   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18 
##  26  41 132  30  98  91 130  39  75  10  21  52  39   6  11  19   8   8

Export Final Target Clusters

df_wui %>%
    distinct(target_gene, target_gene_id, sgID_AB) %>%
    inner_join(tibble(sgID_AB=rownames(mat_pca3), cluster_id=clusters_targets_final), by="sgID_AB") %>%
    write_csv(FILE_OUT_TARGETS)

Final Heatmap

pheatmap(zdwui_target_gene_final[idx_clusters_targets_final, idx_clusters_genes_final], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=1,
         annotation_row=df_row_annots_final, 
         annotation_col=df_col_annots,
         show_colnames=FALSE, show_rownames=FALSE, scale='none', 
         cluster_rows=FALSE, cluster_cols=FALSE, clustering_method="complete",
         gaps_row=cumsum(table(clusters_targets_final)),
         gaps_col=cumsum(table(clusters_genes_final)) %>% `[`(!duplicated(.)),
         cutree_rows=NA, cutree_cols=NA)

Final Joint Plot

pheatmap(zdwui_target_gene_final[idx_clusters_targets_final, idx_clusters_genes_final], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=1,
         annotation_row=df_row_annots_final, 
         annotation_col=df_col_annots,
         show_colnames=TRUE, show_rownames=TRUE, scale='none', 
         labels_row=sgid2gene[idx_clusters_targets_final],
         labels_col=ens2gene[idx_clusters_genes_final],
         cluster_rows=FALSE, cluster_cols=FALSE, clustering_method="complete",
         gaps_row=cumsum(table(clusters_targets_final)),
         gaps_col=cumsum(table(clusters_genes_final)) %>% `[`(!duplicated(.)),
         cutree_rows=NA, cutree_cols=NA,
         filename=FILE_OUT_FINAL, width=16, height=16)
saveRDS(t(dwui_gene_target), RDS_DWUI)
saveRDS(t(zdwui_gene_target), RDS_ZDWUI)

Runtime Details

Session Info

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] bluster_1.4.0   pheatmap_1.0.12 forcats_0.5.1   stringr_1.4.0  
##  [5] dplyr_1.0.8     purrr_0.3.4     readr_2.1.1     tidyr_1.1.4    
##  [9] tibble_3.1.7    ggplot2_3.3.5   tidyverse_1.3.1 magrittr_2.0.3 
## 
## loaded via a namespace (and not attached):
##  [1] matrixStats_0.61.0   fs_1.5.2             bit64_4.0.5         
##  [4] lubridate_1.8.0      RColorBrewer_1.1-2   httr_1.4.2          
##  [7] tools_4.1.1          backports_1.4.0      bslib_0.3.1         
## [10] utf8_1.2.2           R6_2.5.1             irlba_2.3.5         
## [13] DBI_1.1.1            BiocGenerics_0.40.0  colorspace_2.0-2    
## [16] withr_2.4.3          tidyselect_1.1.1     bit_4.0.4           
## [19] compiler_4.1.1       cli_3.3.0            rvest_1.0.2         
## [22] BiocNeighbors_1.12.0 xml2_1.3.3           DelayedArray_0.20.0 
## [25] sass_0.4.0           scales_1.1.1         digest_0.6.29       
## [28] rmarkdown_2.11       pkgconfig_2.0.3      htmltools_0.5.2     
## [31] MatrixGenerics_1.6.0 dbplyr_2.1.1         fastmap_1.1.0       
## [34] highr_0.9            rlang_1.0.2          readxl_1.3.1        
## [37] rstudioapi_0.13      farver_2.1.0         jquerylib_0.1.4     
## [40] generics_0.1.1       jsonlite_1.7.2       vroom_1.5.7         
## [43] BiocParallel_1.28.0  BiocSingular_1.10.0  Matrix_1.3-4        
## [46] Rcpp_1.0.7           munsell_0.5.0        S4Vectors_0.32.0    
## [49] fansi_0.5.0          lifecycle_1.0.1      stringi_1.7.6       
## [52] yaml_2.2.1           grid_4.1.1           parallel_4.1.1      
## [55] crayon_1.4.2         lattice_0.20-45      haven_2.4.3         
## [58] beachmat_2.10.0      hms_1.1.1            knitr_1.39          
## [61] pillar_1.7.0         igraph_1.2.9         ScaledMatrix_1.2.0  
## [64] stats4_4.1.1         reprex_2.0.1         glue_1.6.2          
## [67] evaluate_0.15        modelr_0.1.8         vctrs_0.4.1         
## [70] tzdb_0.2.0           cellranger_1.1.0     gtable_0.3.0        
## [73] assertthat_0.2.1     xfun_0.30            rsvd_1.0.5          
## [76] broom_0.8.0          IRanges_2.28.0       cluster_2.1.2       
## [79] ellipsis_0.3.2

Conda Environment

## Conda Environment YAML
name: base
channels:
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - anaconda-client=1.8.0=pyhd8ed1ab_0
  - anaconda-project=0.10.2=pyhd8ed1ab_0
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  - awscli=1.25.79=py39h6e9494a_0
  - backports=1.0=py_2
  - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
  - backports.zoneinfo=0.2.1=py39h701faf5_5
  - bagit=1.8.1=pyhd8ed1ab_0
  - bagit-profile=1.3.1=pyhd8ed1ab_0
  - bdbag=1.6.1=pyhd8ed1ab_0
  - beautifulsoup4=4.9.3=pyhb0f4dca_0
  - blinker=1.4=py_1
  - boa=0.11.0=pyha770c72_3
  - boolean.py=3.7=py_0
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  - brotlipy=0.7.0=py39h63b48b0_1004
  - bzip2=1.0.8=h0d85af4_4
  - c-ares=1.18.1=h0d85af4_0
  - ca-certificates=2022.9.24=h033912b_0
  - cachecontrol=0.12.11=pyhd8ed1ab_0
  - cairo=1.16.0=he43a7df_1008
  - cctools=973.0.1=hd9211c8_2
  - cctools_osx-64=973.0.1=h3e07e27_2
  - certifi=2022.9.24=pyhd8ed1ab_0
  - cffi=1.15.1=py39hae9ecf2_0
  - chardet=5.0.0=py39h6e9494a_0
  - charset-normalizer=2.0.0=pyhd8ed1ab_0
  - click=8.1.3=py39h6e9494a_0
  - clyent=1.2.2=py_1
  - colorama=0.4.3=py_0
  - commonmark=0.9.1=py_0
  - conda=4.14.0=py39h6e9494a_0
  - conda-build=3.21.9=py39h6e9494a_1
  - conda-forge-pinning=2021.10.10.22.03.30=hd8ed1ab_0
  - conda-libmamba-solver=22.6.0=pyhd8ed1ab_0
  - conda-pack=0.6.0=pyhd3deb0d_0
  - conda-package-handling=1.9.0=py39ha30fb19_0
  - conda-smithy=3.17.2=pyhd8ed1ab_0
  - conda-standalone=4.10.3=h694c41f_0
  - conda-suggest=0.1.1=pyh9f0ad1d_0
  - conda-suggest-conda-forge=2021.8.24=h694c41f_0
  - conda-verify=3.1.1=py39h6e9494a_1004
  - constructor=3.3.1=py39h6e9494a_0
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  - deprecated=1.2.12=pyh44b312d_0
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  - expat=2.4.1=he49afe7_0
  - ffq=0.2.1=pyhdfd78af_0
  - filelock=3.0.12=pyh9f0ad1d_0
  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
  - font-ttf-inconsolata=3.000=h77eed37_0
  - font-ttf-source-code-pro=2.038=h77eed37_0
  - font-ttf-ubuntu=0.83=hab24e00_0
  - fontconfig=2.13.1=h10f422b_1005
  - fonts-conda-ecosystem=1=0
  - fonts-conda-forge=1=0
  - freetype=2.10.4=h4cff582_1
  - fribidi=1.0.10=hbcb3906_0
  - frozendict=2.3.4=py39h701faf5_0
  - future=0.18.2=py39h6e9494a_5
  - gawk=5.1.0=h8a989fb_0
  - gdk-pixbuf=2.42.6=h2e6141f_0
  - gettext=0.19.8.1=hd1a6beb_1008
  - giflib=5.2.1=hbcb3906_2
  - git=2.33.0=pl5321h9a53687_2
  - git-lfs=2.13.3=h694c41f_0
  - gitdb=4.0.7=pyhd8ed1ab_0
  - gitpython=3.1.18=pyhd8ed1ab_0
  - glib=2.70.2=hcf210ce_0
  - glib-tools=2.70.2=hcf210ce_0
  - glob2=0.7=py_0
  - globus-sdk=2.0.1=pyhd8ed1ab_0
  - gmp=6.2.1=h2e338ed_0
  - gnutls=3.6.13=h756fd2b_1
  - graphite2=1.3.13=h2e338ed_1001
  - harfbuzz=2.9.0=h159f659_0
  - htop=3.2.1=h398481e_0
  - htslib=1.15=hc057d7f_0
  - hub=2.14.2=hc7d050b_0
  - icu=68.1=h74dc148_0
  - idna=3.1=pyhd3deb0d_0
  - importlib-metadata=4.11.4=py39h6e9494a_0
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  - importlib_resources=5.4.0=pyhd8ed1ab_0
  - inotify_simple=1.3.5=pyha770c72_3
  - ipython_genutils=0.2.0=py_1
  - isodate=0.6.0=py_1
  - jbig=2.1=h0d85af4_2003
  - jinja2=3.0.1=pyhd8ed1ab_0
  - jmespath=0.10.0=pyh9f0ad1d_0
  - joblib=1.0.1=pyhd8ed1ab_0
  - jpeg=9d=hbcb3906_0
  - json5=0.9.5=pyh9f0ad1d_0
  - jsonschema=4.3.1=pyhd8ed1ab_0
  - jupyter_core=4.11.1=py39h6e9494a_0
  - krb5=1.19.3=hb49756b_0
  - ld64=609=hd2e7500_2
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prefix: /Users/mfansler/miniconda3